10,871 research outputs found

    Using EEG and NIRS for brain-computer interface and cognitive performance measures: a pilot study

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    This study addresses two important problem statements, namely, selection of training datasets for online Brain-Computer Interface (BCI) classifier training and determination of participant concentration levels during an experiment. The work also attempted a pilot study to integrate electroencephalograms (EEGs) and Near Infra Red Spectroscopy (NIRS) for possible applications such as the BCI and for measuring cognitive levels. Two experiments are presented, the first being a mathematical task interleaved with rest states using NIRS only. In the next, integration of the EEG-NIRS with reference to P300-based BCI systems as well as the experimental conditions designed to elicit the concentration levels (denoted as ON and OFF states here) during the paradigm, are presented. The first experiment indicates that NIRS can be used to differentiate a concentrated (i.e., mental activity) level from the rest. However, the second experiment reveals statistically significant results using the EEG only. We present details about the equipment used, the participants as well as the signal processing and machine learning techniques implemented to analyse the EEG and NIRS data. After discussing the results, we conclude by describing the research scope as well as the possible pitfalls in this work from a NIRS viewpoint, which presents an opportunity for future research exploration for BCI and cognitive performance measures

    Using Noninvasive Brain Measurement to Explore the Psychological Effects of Computer Malfunctions on Users during Human-Computer Interactions

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    In today’s technologically driven world, there is a need to better understand the ways that common computer malfunctions affect computer users. These malfunctions may have measurable influences on computer user’s cognitive, emotional, and behavioral responses. An experiment was conducted where participants conducted a series of web search tasks while wearing functional nearinfrared spectroscopy (fNIRS) and galvanic skin response sensors. Two computer malfunctions were introduced during the sessions which had the potential to influence correlates of user trust and suspicion. Surveys were given after each session to measure user’s perceived emotional state, cognitive load, and perceived trust. Results suggest that fNIRS can be used to measure the different cognitive and emotional responses associated with computer malfunctions. These cognitive and emotional changes were correlated with users’ self-report levels of suspicion and trust, and they in turn suggest future work that further explores the capability of fNIRS for the measurement of user experience during human-computer interactions

    Brain-Switches for Asynchronous Brain−Computer Interfaces: A Systematic Review

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    A brain–computer interface (BCI) has been extensively studied to develop a novel communication system for disabled people using their brain activities. An asynchronous BCI system is more realistic and practical than a synchronous BCI system, in that, BCI commands can be generated whenever the user wants. However, the relatively low performance of an asynchronous BCI system is problematic because redundant BCI commands are required to correct false-positive operations. To significantly reduce the number of false-positive operations of an asynchronous BCI system, a two-step approach has been proposed using a brain-switch that first determines whether the user wants to use an asynchronous BCI system before the operation of the asynchronous BCI system. This study presents a systematic review of the state-of-the-art brain-switch techniques and future research directions. To this end, we reviewed brain-switch research articles published from 2000 to 2019 in terms of their (a) neuroimaging modality, (b) paradigm, (c) operation algorithm, and (d) performance

    In silico vs. Over the Clouds: On-the-Fly Mental State Estimation of Aircraft Pilots, Using a Functional Near Infrared Spectroscopy Based Passive-BCI

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    There is growing interest for implementing tools to monitor cognitive performance in naturalistic work and everyday life settings. The emerging field of research, known as neuroergonomics, promotes the use of wearable and portable brain monitoring sensors such as functional near infrared spectroscopy (fNIRS) to investigate cortical activity in a variety of human tasks out of the laboratory. The objective of this study was to implement an on-line passive fNIRS-based brain computer interface to discriminate two levels of working memory load during highly ecological aircraft piloting tasks. Twenty eight recruited pilots were equally split into two groups (flight simulator vs. real aircraft). In both cases, identical approaches and experimental stimuli were used (serial memorization task, consisting in repeating series of pre-recorded air traffic control instructions, easy vs. hard). The results show pilots in the real flight condition committed more errors and had higher anterior prefrontal cortex activation than pilots in the simulator, when completing cognitively demanding tasks. Nevertheless, evaluation of single trial working memory load classification showed high accuracy (>76%) across both experimental conditions. The contributions here are two-fold. First, we demonstrate the feasibility of passively monitoring cognitive load in a realistic and complex situation (live piloting of an aircraft). In addition, the differences in performance and brain activity between the two experimental conditions underscore the need for ecologically-valid investigations

    Real-Time State Estimation in a Flight Simulator Using fNIRS

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    Working memory is a key executive function for flying an aircraft. This function is particularly critical when pilots have to recall series of air traffic control instructions. However, working memory limitations may jeopardize flight safety. Since the functional near-infrared spectroscopy (fNIRS) method seems promising for assessing working memory load, our objective is to implement an on-line fNIRS-based inference system that integrates two complementary estimators. The first estimator is a real-time state estimation MACD-based algorithm dedicated to identifying the pilot’s instantaneous mental state (not-on-task vs. on-task). It does not require a calibration process to perform its estimation. The second estimator is an on-line SVM-based classifier that is able to discriminate task difficulty (low working memory load vs. high working memory load). These two estimators were tested with 19 pilots who were placed in a realistic flight simulator and were asked to recall air traffic control instructions. We found that the estimated pilot’s mental state matched significantly better than chance with the pilot’s real state (62% global accuracy, 58% specificity, and 72% sensitivity). The second estimator, dedicated to assessing single trial working memory loads, led to 80% classification accuracy, 72% specificity, and 89% sensitivity. These two estimators establish reusable blocks for further fNIRS-based passive brain computer interface development

    Prefrontal cortex activation upon a demanding virtual hand-controlled task: A new frontier for neuroergonomics

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    open9noFunctional near-infrared spectroscopy (fNIRS) is a non-invasive vascular-based functional neuroimaging technology that can assess, simultaneously from multiple cortical areas, concentration changes in oxygenated-deoxygenated hemoglobin at the level of the cortical microcirculation blood vessels. fNIRS, with its high degree of ecological validity and its very limited requirement of physical constraints to subjects, could represent a valid tool for monitoring cortical responses in the research field of neuroergonomics. In virtual reality (VR) real situations can be replicated with greater control than those obtainable in the real world. Therefore, VR is the ideal setting where studies about neuroergonomics applications can be performed. The aim of the present study was to investigate, by a 20-channel fNIRS system, the dorsolateral/ventrolateral prefrontal cortex (DLPFC/VLPFC) in subjects while performing a demanding VR hand-controlled task (HCT). Considering the complexity of the HCT, its execution should require the attentional resources allocation and the integration of different executive functions. The HCT simulates the interaction with a real, remotely-driven, system operating in a critical environment. The hand movements were captured by a high spatial and temporal resolution 3-dimensional (3D) hand-sensing device, the LEAP motion controller, a gesture-based control interface that could be used in VR for tele-operated applications. Fifteen University students were asked to guide, with their right hand/forearm, a virtual ball (VB) over a virtual route (VROU) reproducing a 42 m narrow road including some critical points. The subjects tried to travel as long as possible without making VB fall. The distance traveled by the guided VB was 70.2 ± 37.2 m. The less skilled subjects failed several times in guiding the VB over the VROU. Nevertheless, a bilateral VLPFC activation, in response to the HCT execution, was observed in all the subjects. No correlation was found between the distance traveled by the guided VB and the corresponding cortical activation. These results confirm the suitability of fNIRS technology to objectively evaluate cortical hemodynamic changes occurring in VR environments. Future studies could give a contribution to a better understanding of the cognitive mechanisms underlying human performance either in expert or non-expert operators during the simulation of different demanding/fatiguing activities.openCarrieri, Marika; Petracca, Andrea; Lancia, Stefania; Basso Moro, Sara; Brigadoi, Sabrina; Spezialetti, Matteo; Ferrari, Marco; Placidi, Giuseppe; Quaresima, ValentinaCarrieri, Marika; Petracca, Andrea; Lancia, Stefania; BASSO MORO, Sara; Brigadoi, Sabrina; Spezialetti, Matteo; Ferrari, Marco; Placidi, Giuseppe; Quaresima, Valentin

    Convolutional Neural Network for Functional Near-Infrared Spectroscopy-Based Brain-Computer Interface

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    Brain-computer interface (BCI) is a communication system that translates the brain signal directly to a computer or external devices. It is a promising solution for the patients with neurological disorders as the system is able to restore the movement ability. Various neuroimaging modalities have been utilized for brain signal acquisition, however, functional near-infrared spectroscopy (fNIRS) provides many advantages over other modalities. Hence, it has gained attention for implementing in BCI system. For developing BCI system, the appropriate machine learning algorithm and discriminating features from the hemodynamic response signal are desired, as the previous studies have reported the performance enhancement of fNIRS-based BCI in terms of classification accuracy by focusing on the classifier as well as signal features. The aim of this thesis is to improve the classification accuracy in fNIRS-based BCI by classifying and extracting feature automatically. The convolutional neural network (CNN) was applied owing to the automatic feature extractor and classifier instead of manual feature extraction in the conventional methods. In the experiment, four healthy subjects were measured the hemodynamic response signal evoked by performing tasks including rest, right and left hand motor executions. The conventional methods of fNIRS-based BCI using signal mean, slope, peak, variance, skewness, and kurtosis as the features, and support vector machine (SVM) and artificial neural network (ANN) as the classification methods were compared with CNN-based method. The results show the improvement of classification accuracy of CNN-based method over SVM-based and ANN-based method 6.92% and 3.75%, respectively. The main contributions of this thesis are (1) the promising feature extraction and classification method for fNIRS-based BCI using CNN and (2) the analysis of the feature extracted by conventional methods and convolutional filter of the CNN. ⓒ 2017 DGISTprohibitionI. INTRODUCTION 1-- 1. Motivation 1-- 2. Objective 2-- II. BACKGROUND AND RELATEDWORK 4-- 1. Functional Near-Infrared Spectroscopy (fNIRS) 4-- 2. fNIRS-based BCI 5-- 3. Feature Extraction and Classification 6-- 3.1 Feature Extraction 6-- 3.2 Support Vector Machine (SVM) 7-- 3.3 Artificial Neural Network (ANN) 7-- 3.4 Convolutional Neural Network (CNN) 9-- 4. Evaluation 11-- III. METHOD 12-- 1. Participants 12-- 2. Data Acquisition 12-- 3. Experimental Procedure 12-- 4. Preprocessing 13-- 4.1 Concentration Changes of Hemoglobin 13-- 4.2 Filtering 14-- 5. Feature Extraction and Classification 16-- 5.1 Conventional Method 16-- 5.2 Proposed Structures of CNN 17-- 6. Feature Visualization 20-- IV. RESULTS AND DISCUSSIONS 23-- 1. Measured Hemodynamic Responses 23-- 2. Classification Accuracy 24-- 3. Feature Visualization 26-- 4. Future Work 28-- V. CONCLUSION 30-- References 31-- Acknowledgments 38-- Curriculum Vitae 39MasterdCollectio
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